





import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase								=  logging.get_logger()



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase = True  ):
   '''simple docstring'''


   print(f"""Converting {name}..."""  )

   with torch.no_grad():
      if hidden_sizes == 1_2_8:
         if name[-1] == "S":
            A_  :					List[Any]         =						timm.create_model("""levit_128s"""	,pretrained=__snake_case  )
         else:
            A_  :					Union[str, Any]         =						timm.create_model("""levit_128"""	,pretrained=__snake_case  )
      if hidden_sizes == 1_9_2:
         A_  :					Union[str, Any]         =						timm.create_model("""levit_192"""	,pretrained=__snake_case  )
      if hidden_sizes == 2_5_6:
         A_  :					Union[str, Any]         =						timm.create_model("""levit_256"""	,pretrained=__snake_case  )
      if hidden_sizes == 3_8_4:
         A_  :					Optional[int]         =						timm.create_model("""levit_384"""	,pretrained=__snake_case  )

      from_model.eval()
      A_  :					Union[str, Any]         =						LevitForImageClassificationWithTeacher(__snake_case  ).eval()
      A_  :					Union[str, Any]         =						OrderedDict()

      A_  :					Union[str, Any]         =						from_model.state_dict()
      A_  :					List[str]         =						list(from_model.state_dict().keys()  )
      A_  :					Union[str, Any]         =						list(our_model.state_dict().keys()  )
      print(len(__snake_case  )	,len(__snake_case  )  )
      for i in range(len(__snake_case  )  ):
         A_  :					int         =						weights[og_keys[i]]
      our_model.load_state_dict(__snake_case  )

      A_  :					Optional[Any]         =						torch.randn((2, 3, 2_2_4, 2_2_4)  )
      A_  :					int         =						from_model(__snake_case  )
      A_  :					List[str]         =						our_model(__snake_case  ).logits

   assert torch.allclose(__snake_case	,__snake_case  ), "The model logits don't match the original one."

   A_  :					Optional[Any]         =						name
   print(__snake_case  )

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name  )
      A_  :					Tuple         =						LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name  )

      print(f"""Pushed {checkpoint_name}"""  )



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase = None	,_lowerCAmelCase = True  ):
   '''simple docstring'''


   A_  :					Optional[Any]         =						"""imagenet-1k-id2label.json"""
   A_  :					Union[str, Any]         =						1_0_0_0
   A_  :					int         =						(1, num_labels)

   A_  :					int         =						"""huggingface/label-files"""
   A_  :					List[Any]         =						num_labels
   A_  :					Dict         =						json.load(open(hf_hub_download(__snake_case	,__snake_case	,repo_type="""dataset"""  )	,"""r"""  )  )
   A_  :					List[Any]         =						{int(__snake_case  ): v for k, v in idalabel.items()}

   A_  :					Tuple         =						idalabel
   A_  :					List[str]         =						{v: k for k, v in idalabel.items()}

   A_  :					int         =						partial(__snake_case	,num_labels=__snake_case	,idalabel=__snake_case	,labelaid=__snake_case  )

   A_  :					List[Any]         =						{
       """levit-128S""": 1_2_8,
       """levit-128""": 1_2_8,
       """levit-192""": 1_9_2,
       """levit-256""": 2_5_6,
       """levit-384""": 3_8_4,
   }

   A_  :					str         =						{
       """levit-128S""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_2_8, 2_5_6, 3_8_4]	,num_attention_heads=[4, 6, 8]	,depths=[2, 3, 4]	,key_dim=[1_6, 1_6, 1_6]	,drop_path_rate=0	,),
       """levit-128""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_2_8, 2_5_6, 3_8_4]	,num_attention_heads=[4, 8, 1_2]	,depths=[4, 4, 4]	,key_dim=[1_6, 1_6, 1_6]	,drop_path_rate=0	,),
       """levit-192""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_9_2, 2_8_8, 3_8_4]	,num_attention_heads=[3, 5, 6]	,depths=[4, 4, 4]	,key_dim=[3_2, 3_2, 3_2]	,drop_path_rate=0	,),
       """levit-256""": ImageNetPreTrainedConfig(
           hidden_sizes=[2_5_6, 3_8_4, 5_1_2]	,num_attention_heads=[4, 6, 8]	,depths=[4, 4, 4]	,key_dim=[3_2, 3_2, 3_2]	,drop_path_rate=0	,),
       """levit-384""": ImageNetPreTrainedConfig(
           hidden_sizes=[3_8_4, 5_1_2, 7_6_8]	,num_attention_heads=[6, 9, 1_2]	,depths=[4, 4, 4]	,key_dim=[3_2, 3_2, 3_2]	,drop_path_rate=0.1	,),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]	,__snake_case	,names_to_config[model_name]	,__snake_case	,__snake_case  )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]	,__snake_case	,__snake_case	,__snake_case	,__snake_case  )
   return config, expected_shape


if __name__ == "__main__":
      _lowerCAmelCase								=  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default=None,
          type=str,
          help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default="""levit-dump-folder/""",
          type=Path,
          required=False,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
      parser.add_argument(
          """--no-push_to_hub""",
          dest="""push_to_hub""",
          action="""store_false""",
          help="""Do not push model and image processor to the hub""",
      )

      _lowerCAmelCase								=  parser.parse_args()
      _lowerCAmelCase								=  args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)














"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
 from transformers.trainer import DEFAULT_CALLBACKS

 from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    []







    def 				a__			(		self :	Tuple				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	int				,    **UpperCamelCase_ :	int ):





           '''simple docstring'''

           self.events.append('on_init_end' )







    def 				a__			(		self :	Any				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	List[str]				,    **UpperCamelCase_ :	str ):





           '''simple docstring'''

           self.events.append('on_train_begin' )







    def 				a__			(		self :	str				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	Optional[int]				,    **UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           self.events.append('on_train_end' )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Tuple				,    **UpperCamelCase_ :	Optional[Any] ):





           '''simple docstring'''

           self.events.append('on_epoch_begin' )







    def 				a__			(		self :	Optional[int]				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Tuple				,    **UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           self.events.append('on_epoch_end' )







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	Union[str, Any]				,    **UpperCamelCase_ :	Optional[Any] ):





           '''simple docstring'''

           self.events.append('on_step_begin' )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Any				,    **UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           self.events.append('on_step_end' )







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Tuple				,    **UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           self.events.append('on_evaluate' )







    def 				a__			(		self :	str				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	Dict				,    **UpperCamelCase_ :	Any ):





           '''simple docstring'''

           self.events.append('on_predict' )







    def 				a__			(		self :	Any				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	str				,    **UpperCamelCase_ :	Optional[int] ):





           '''simple docstring'''

           self.events.append('on_save' )







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Union[str, Any]				,    **UpperCamelCase_ :	Optional[int] ):





           '''simple docstring'''

           self.events.append('on_log' )







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	str				,    **UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           self.events.append('on_prediction_step' )



@require_torch
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    tempfile.mkdtemp()







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           shutil.rmtree(self.output_dir )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Optional[int]=0				,    UpperCamelCase_ :	List[Any]=0				,    UpperCamelCase_ :	Optional[int]=6_4				,    UpperCamelCase_ :	Optional[int]=6_4				,    UpperCamelCase_ :	List[Any]=None				,    UpperCamelCase_ :	Optional[Any]=False				,    **UpperCamelCase_ :	str ):





           '''simple docstring'''

           __magic_name__      				=    RegressionDataset(length=UpperCamelCase_ )
           __magic_name__      				=    RegressionDataset(length=UpperCamelCase_ )
           __magic_name__      				=    RegressionModelConfig(a=UpperCamelCase_				,    b=UpperCamelCase_ )
           __magic_name__      				=    RegressionPreTrainedModel(UpperCamelCase_ )

           __magic_name__      				=    TrainingArguments(self.output_dir				,    disable_tqdm=UpperCamelCase_				,    report_to=[]				,    **UpperCamelCase_ )
           return Trainer(
               UpperCamelCase_				,    UpperCamelCase_				,    train_dataset=UpperCamelCase_				,    eval_dataset=UpperCamelCase_				,    callbacks=UpperCamelCase_				,    )







    def 				a__			(		self :	Optional[int]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           self.assertEqual(len(UpperCamelCase_ )				,    len(UpperCamelCase_ ) )

           # Order doesn't matter
           __magic_name__      				=    sorted(UpperCamelCase_				,    key=lambda UpperCamelCase_ : cb.__name__ if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else cb.__class__.__name__ )
           __magic_name__      				=    sorted(UpperCamelCase_				,    key=lambda UpperCamelCase_ : cb.__name__ if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else cb.__class__.__name__ )

           for cba, cba in zip(UpperCamelCase_				,    UpperCamelCase_ ):
                  if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) and isinstance(UpperCamelCase_				,    UpperCamelCase_ ):
                         self.assertEqual(UpperCamelCase_				,    UpperCamelCase_ )
                  elif isinstance(UpperCamelCase_				,    UpperCamelCase_ ) and not isinstance(UpperCamelCase_				,    UpperCamelCase_ ):
                         self.assertEqual(UpperCamelCase_				,    cba.__class__ )
                  elif not isinstance(UpperCamelCase_				,    UpperCamelCase_ ) and isinstance(UpperCamelCase_				,    UpperCamelCase_ ):
                         self.assertEqual(cba.__class__				,    UpperCamelCase_ )
                  else:
                         self.assertEqual(UpperCamelCase_				,    UpperCamelCase_ )







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	Any ):





           '''simple docstring'''

           __magic_name__      				=    ['on_init_end', 'on_train_begin']
           __magic_name__      				=    0
           __magic_name__      				=    len(trainer.get_eval_dataloader() )
           __magic_name__      				=    ['on_prediction_step'] * len(trainer.get_eval_dataloader() ) + ['on_log', 'on_evaluate']
           for _ in range(trainer.state.num_train_epochs ):
                  expected_events.append('on_epoch_begin' )
                  for _ in range(UpperCamelCase_ ):
                         step += 1
                         expected_events += ["on_step_begin", "on_step_end"]
                         if step % trainer.args.logging_steps == 0:
                                expected_events.append('on_log' )
                         if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
                                expected_events += evaluation_events.copy()
                         if step % trainer.args.save_steps == 0:
                                expected_events.append('on_save' )
                  expected_events.append('on_epoch_end' )
                  if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
                         expected_events += evaluation_events.copy()
           expected_events += ["on_log", "on_train_end"]
           return expected_events







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.get_trainer()
           __magic_name__      				=    DEFAULT_CALLBACKS.copy() + [ProgressCallback]
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           # Callbacks passed at init are added to the default callbacks
           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback] )
           expected_callbacks.append(UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
           __magic_name__      				=    self.get_trainer(disable_tqdm=UpperCamelCase_ )
           __magic_name__      				=    DEFAULT_CALLBACKS.copy() + [PrinterCallback]
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           __magic_name__      				=    DEFAULT_CALLBACKS.copy() + [ProgressCallback]
           __magic_name__      				=    self.get_trainer()

           # We can add, pop, or remove by class name
           trainer.remove_callback(UpperCamelCase_ )
           expected_callbacks.remove(UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           __magic_name__      				=    self.get_trainer()
           __magic_name__      				=    trainer.pop_callback(UpperCamelCase_ )
           self.assertEqual(cb.__class__				,    UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           trainer.add_callback(UpperCamelCase_ )
           expected_callbacks.insert(0				,    UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           # We can also add, pop, or remove by instance
           __magic_name__      				=    self.get_trainer()
           __magic_name__      				=    trainer.callback_handler.callbacks[0]
           trainer.remove_callback(UpperCamelCase_ )
           expected_callbacks.remove(UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           __magic_name__      				=    self.get_trainer()
           __magic_name__      				=    trainer.callback_handler.callbacks[0]
           __magic_name__      				=    trainer.pop_callback(UpperCamelCase_ )
           self.assertEqual(UpperCamelCase_				,    UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           trainer.add_callback(UpperCamelCase_ )
           expected_callbacks.insert(0				,    UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           import warnings

           # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
           warnings.simplefilter(action='ignore'				,    category=UpperCamelCase_ )

           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback] )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           # Independent log/save/eval
           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback]				,    logging_steps=5 )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback]				,    save_steps=5 )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback]				,    eval_steps=5				,    evaluation_strategy='steps' )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback]				,    evaluation_strategy='epoch' )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           # A bit of everything
           __magic_name__      				=    self.get_trainer(
               callbacks=[MyTestTrainerCallback]				,    logging_steps=3				,    save_steps=1_0				,    eval_steps=5				,    evaluation_strategy='steps'				,    )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           # warning should be emitted for duplicated callbacks
           with patch('transformers.trainer_callback.logger.warning' ) as warn_mock:
                  __magic_name__      				=    self.get_trainer(
                      callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]				,    )
                  assert str(UpperCamelCase_ ) in warn_mock.call_args[0][0]